During 1 month, papers accepted at GECCO1’4 will be freely available. Thus, you can get and read our papers:
- “Enforcing corporate security policies via computational intelligence techniques” by Antonio M. Mora, Paloma De las Cuevas, Juan Julián Merelo, Sergio Zamarripa, Anna I. Esparcia-Alcázar (doi: 10.1145/2598394.2605438) at http://goo.gl/33gWES
- “A methodology for designing emergent literary backstories on non-player characters using genetic algorithms”, by Rubén Héctor García-Ortega, Pablo García-Sánchez, Antonio Miguel Mora, Juan Julián Merelo (doi: 10.1145/2598394.2598482) at http://goo.gl/9CEcMc
We cordially invite you to attend the following two-presentations on Spatially Structured Metaheuristics. This mini-workshop will be held at 11.30 a.m. in the CITIC-UGR building (June 26th, 2014).
Spatially Structured Metaheuristics: Principles and Practical Applications
by Juan Luis Jiménez Laredo (University of Luxembourg)
A relevant number of metaheuristics are based on population. Although conventions may establish different names, individuals in evolutionary algorithms, ants in ant colony optimization or particles in particle swarm optimization belong to the same side of a coin: they are all atomic elements of the population (a.k.a. building-blocks). In this context, spatially structured metaheuristics investigate how to improve the performance of metaheuristics by confining these elements in neighborhoods. This talk aims at presenting the working principles of spatially structured metaheuristics and practical applications to enhance diversity, scalability and robustness.
Spatially Structured Metaheuristics: Dynamic and Self-organized Topologies
by Carlos M. Fernandes (University of Lisbon)
Population based metaheuristics are computational search or optimization methods that use a population of possible solutions to a problem. These solutions are able communicate, interact and/or evolve. Two types of strategies for structuring population are possible. In panmictic populations, every individual is allowed to interact with every other individual. In non-panmictic metaheuristics, also called spatially structured population-based metaheuristics, the interaction is restricted to a pre-defined or evolving structure (network). Traditional spatially structured metaheuristics are built on pre-defined static networks of acquaintances over which individuals can interact. However, alternative strategies that overcome some of the difficulties and limitations of static networks (extra design and tuning effort, ad hoc decision policies, rigid connectivity, and lack of feedback from the problem structure and search process) are possible. This talk discusses dynamic topologies for spatially structured metaheuristics and describes a new model for structuring populations into partially connected and self-organized networks. Recent applications of the model on Evolutionary Algorithms and Particle Swarm Optimization are given and discussed.
The authors of the paper are J.J. Merelo, Pedro Castillo, Antonio Mora and Víctor Rivas from GeNeura Team, and Anna Esparcia-Alcazar, from S2 Grupo
Recientemente hemos inaugurado en la web de Sipesca  una sección con las apariciones en prensa del proyecto, para recoger la difusión que está teniendo el proyecto en los medios.
by Carlos Fernandes, Juan Laredo, Juan Merelo, Carlos Cotta, Agostinho Rosa
This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary
Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive and NP-hard fitness landscapes. A general framework for modelling partially connected topologies is proposed and three different schemes are tested. The results show that the structures improve the rate of convergence to global optima when compared to cEAs with standard topologies (ring, rectangular and square) on quasi-deceptive, deceptive and NP-hard problems. Optimal population size tests demonstrate that the proposed topologies require smaller populations when compared to traditional cEAs.
by Mario Garcia-Valdez, Juan-J. Merelo, Francisco Fernández de Vega
in EvoAPPS posters
In this paper the effect of node unavailability in algorithms using EvoSpace, a pool-based evolutionary algorithm, is assessed. EvoSpace is a framework for developing evolutionary algorithms (EAs) using heterogeneous and unreliable resources. It is based on Linda’s tuple space coordination model. The core elements of EvoSpace are a central repository for the evolving population and remote clients, here called EvoWorkers, which pull random samples of the population to perform on them the basic evolutionary processes (selection, variation and survival), once the work is done, the modified sample is pushed back to the central population. To address the problem of unreliable EvoWorkers, EvoSpace uses a simple re-insertion algorithm using copies of samples stored in a global queue which also prevents the starvation of the population pool. Using a benchmark problem from the P-Peaks problem generator we have compared two approaches: (i) the re-insertion of previous individuals at the cost of keeping copies of each sample, and a common approach of other pool based EAs, (ii) inserting randomly generated individuals. We found that EvoSpace is fault tolerant to highly unreliable resources and also that the re-insertion algorithm is only needed when the population is near the point of starvation.